SOTAVerified

Self-Supervised Learning

Self-Supervised Learning is proposed for utilizing unlabeled data with the success of supervised learning. Producing a dataset with good labels is expensive, while unlabeled data is being generated all the time. The motivation of Self-Supervised Learning is to make use of the large amount of unlabeled data. The main idea of Self-Supervised Learning is to generate the labels from unlabeled data, according to the structure or characteristics of the data itself, and then train on this unsupervised data in a supervised manner. Self-Supervised Learning is wildly used in representation learning to make a model learn the latent features of the data. This technique is often employed in computer vision, video processing and robot control.

Source: Self-supervised Point Set Local Descriptors for Point Cloud Registration

Image source: LeCun

Papers

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Showing 1751–1800 of 5044 papers

TitleStatusHype
Investigating Power laws in Deep Representation Learning—0
GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis—0
Invisible Backdoor Attacks Using Data Poisoning in the Frequency Domain—0
Joint Embedding Self-Supervised Learning in the Kernel Regime—0
GenDistiller: Distilling Pre-trained Language Models based on an Autoregressive Generative Model—0
GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition—0
Grow and Merge: A Unified Framework for Continuous Categories Discovery—0
Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning—0
GelFlow: Self-supervised Learning of Optical Flow for Vision-Based Tactile Sensor Displacement Measurement—0
GEDI: GEnerative and DIscriminative Training for Self-Supervised Learning—0
AI Foundation Models in Remote Sensing: A Survey—0
Action Spotting and Precise Event Detection in Sports: Datasets, Methods, and Challenges—0
Guided Diffusion from Self-Supervised Diffusion Features—0
Self-supervised learning of hologram reconstruction using physics consistency—0
Continual Contrastive Finetuning Improves Low-Resource Relation Extraction—0
Guillotine Regularization: Why removing layers is needed to improve generalization in Self-Supervised Learning—0
Gaussian Masked Autoencoders—0
Gaussian2Scene: 3D Scene Representation Learning via Self-supervised Learning with 3D Gaussian Splatting—0
Continual Barlow Twins: continual self-supervised learning for remote sensing semantic segmentation—0
A Theory of Self-Supervised Framework for Few-Shot Learning—0
Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signals—0
Gated Self-supervised Learning For Improving Supervised Learning—0
HandMIM: Pose-Aware Self-Supervised Learning for 3D Hand Mesh Estimation—0
Contextures: The Mechanism of Representation Learning—0
GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition—0
GANORCON: Are Generative Models Useful for Few-shot Segmentation?—0
Contrastive Self-supervised Learning in Recommender Systems: A Survey—0
HarmonyIQA: Pioneering Benchmark and Model for Image Harmonization Quality Assessment—0
A Theoretical Study of Inductive Biases in Contrastive Learning—0
Contrastive Self-Supervised Learning of Global-Local Audio-Visual Representations—0
Augmented Contrastive Self-Supervised Learning for Audio Invariant Representations—0
HASRD: Hierarchical Acoustic and Semantic Representation Disentanglement—0
​4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection—0
Game State Learning via Game Scene Augmentation—0
Galileo: Learning Global and Local Features in Pretrained Remote Sensing Models—0
HDR Imaging for Dynamic Scenes with Events—0
GaitMorph: Transforming Gait by Optimally Transporting Discrete Codes—0
A theoretically grounded characterization of feature representations—0
AI can evolve without labels: self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation—0
Bag of Image Patch Embedding Behind the Success of Self-Supervised Learning—0
GAIA: A Foundation Model for Operational Atmospheric Dynamics—0
A theoretical framework for self-supervised contrastive learning for continuous dependent data—0
Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report—0
FUSSL: Fuzzy Uncertain Self Supervised Learning—0
Contextualized and Generalized Sentence Representations by Contrastive Self-Supervised Learning: A Case Study on Discourse Relation Analysis—0
A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications—0
Conv1D Energy-Aware Path Planner for Mobile Robots in Unstructured Environments—0
Heterogeneous Space Fusion and Dual-Dimension Attention: A New Paradigm for Speech Enhancement—0
Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensing—0
Fusion of stereo and still monocular depth estimates in a self-supervised learning context—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pretraining: NoneImages & Text57.5—Unverified
2Pretraining: ShEDImages & Text54.3—Unverified
3Pretraining: e-MixImages & Text48.9—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50Accuracy91.7—Unverified
2ResNet18Accuracy91.02—Unverified
3MV-MRAccuracy89.67—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy93.89—Unverified
2ResNet18average top-1 classification accuracy92.58—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy72.51—Unverified
2ResNet18average top-1 classification accuracy69.31—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy82.64—Unverified
2CorInfomax (ResNet18)Top-1 Accuracy80.48—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy51.84—Unverified
2ResNet18average top-1 classification accuracy51.67—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy93.18—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy71.61—Unverified
#ModelMetricClaimedVerifiedStatus
1Hybrid BYOL-S/CvTAccuracy67.2—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy54.86—Unverified